About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008 - Autoformer/README.md at main · mbrukman/Autoformer
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008 - Autoformer/utils at main · Yuxin-Tao/Autoformer
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008 - Autoformer/run.py at main · Yuxin-Tao/Autoformer
The DLinear model uses the decomposition layer from the Autoformer model, which we will introduce later in this post. The authors claim that the DLinear model outperforms the Transformer-based models in time-series forecasting. Is that so? Let's find out....
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008 - Autoformer/environment.yml at main · Yuxin-Tao/Autoformer
Source code of paper: GBT: Two-stage Transformer Framework for Non-stationary Time Series Forecasting. - GBT/FEDformer/Autoformer.py at main · OrigamiSL/GBT
[autoformer] reduced banner size to 800px width (huggingface#1242) 260e88f· Jun 19, 2023 HistoryHistory This branch is 271 commits behind huggingface/blog:main.Folders and files Name Last commit message Last commit date parent directory .. thumbnail.png [autoformer] reduced banner size to ...
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008 - Autoformer/models/Transformer.py at main · thuml/Autoformer
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008 - Autoformer/requirements.txt at main · white-r/Autoformer
{# Architecture parameters 'model':'autoformer', 'mode': 'iterate_windows', 'seq_len': hp.choice('seq_len', [args.seq_len]), 'label_len': hp.choice('label_len', [args.label_len]), 'pred_len': hp.choice('pred_len', [args.horizon]), 'output_attention': hp.choice('output_...